COMPMID-1935 Add support for QASYMM8 in NEQuantizeLayer
Change-Id: I2b63a644d8e34f91c830d9ac398debcbdca3e497
Signed-off-by: John Kesapides <john.kesapides@arm.com>
Reviewed-on: https://review.mlplatform.org/c/829
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp b/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp
index b49400a..136457c 100644
--- a/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,152 +23,140 @@
*/
#include "arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h"
-#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/NEON/NEAsymm.h"
+#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
+#include "arm_compute/core/CPP/Validate.h"
+
#include <arm_neon.h>
using namespace arm_compute;
namespace
{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *min_max)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output, min_max);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() < 3);
-
- if(output->tensor_shape().total_size() > 0)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
- }
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape().total_size() == 0);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
return Status{};
}
-std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *min_max)
+inline const float32x4x4_t load_value(const float *input_ptr)
{
- // Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::U8);
-
- constexpr unsigned int num_elems_processed_per_iteration = 8;
-
- // Configure window
- Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
- AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
- AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
- AccessWindowStatic min_max_access(min_max, 0, 0, 2, min_max->dimension(1));
-
- // Update window and padding
- bool window_changed = update_window_and_padding(win, input_access, output_access, min_max_access);
-
- output_access.set_valid_region(win, input->valid_region());
-
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_tuple(err, win);
+ return { wrapper::vloadq(input_ptr),
+ wrapper::vloadq(input_ptr + 4),
+ wrapper::vloadq(input_ptr + 8),
+ wrapper::vloadq(input_ptr + 12) };
}
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+inline const float32x4x4_t load_value(const float16_t *input_ptr)
+{
+ return { vcvt_f32_f16(wrapper::vload(input_ptr)),
+ vcvt_f32_f16(wrapper::vload(input_ptr + 4)),
+ vcvt_f32_f16(wrapper::vload(input_ptr + 8)),
+ vcvt_f32_f16(wrapper::vload(input_ptr + 12)) };
+}
+
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
} // namespace
NEQuantizationLayerKernel::NEQuantizationLayerKernel()
- : _input(nullptr), _output(nullptr), _min_max(nullptr)
+ : _input(nullptr), _output(nullptr)
{
}
-void NEQuantizationLayerKernel::configure(const ITensor *input, ITensor *output, const ITensor *min_max)
+void NEQuantizationLayerKernel::configure(const ITensor *input, ITensor *output)
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, min_max);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), min_max->info()));
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
- _input = input;
- _output = output;
- _min_max = min_max;
+ _input = input;
+ _output = output;
// Configure kernel window
- auto win_config = validate_and_configure_window(input->info(), output->info(), min_max->info());
+ Window win_config = calculate_max_window(*input->info(), Steps());
- ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
+ Coordinates coord;
+ coord.set_num_dimensions(output->info()->num_dimensions());
+ output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));
- INEKernel::configure(std::get<1>(win_config));
+ INEKernel::configure(win_config);
}
-Status NEQuantizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *min_max)
+Status NEQuantizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, min_max));
- ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), min_max->clone().get())));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
return Status{};
}
+template <typename T>
+void NEQuantizationLayerKernel::quantize(const Window &window, const QuantizationInfo &qinfo)
+{
+ constexpr auto window_step = 16;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+#ifdef __aarch64__
+ constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_EVEN;
+#else //__aarch64__
+ constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_ZERO;
+#endif //__aarch64__
+
+ // Collapse window and reset first dimension to handle tail calculations manually
+ Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
+ win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator input(_input, win_collapsed);
+ Iterator output(_output, win_collapsed);
+ execute_window_loop(win_collapsed, [&](const Coordinates & id)
+ {
+ auto input_ptr = reinterpret_cast<const T *>(input.ptr());
+ auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
+
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step); x += window_step)
+ {
+ wrapper::vstore(&output_ptr[x], vquantize(load_value(&input_ptr[x]), qinfo));
+ }
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ output_ptr[x] = qinfo.quantize(input_ptr[x], rounding_policy);
+ }
+ },
+ input, output);
+}
+
void NEQuantizationLayerKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
- Window window_input_output(window);
- window_input_output.set(3, Window::Dimension(0, 1, 1));
+ const QuantizationInfo &qinfo = _output->info()->quantization_info();
- Window window_min_max;
- window_min_max.use_tensor_dimensions(_min_max->info()->tensor_shape());
- window_min_max.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator input(_input, window_input_output);
- Iterator output(_output, window_input_output);
- Iterator min_max(_min_max, window_min_max);
-
- execute_window_loop(window_min_max, [&](const Coordinates & id_batch)
+ switch(_input->info()->data_type())
{
- // Get the min and max
- float min = *(reinterpret_cast<const float *>(min_max.ptr()) + 0);
- float max = *(reinterpret_cast<const float *>(min_max.ptr()) + 1);
-
- // Saturate the result if min = max
- if(min == max)
- {
- min = 0.0f;
- max = 1.0f;
- }
-
- const float32x4_t vmin = vdupq_n_f32(min);
- const float32x4_t inv_range = vdupq_n_f32(1.0f / (max - min));
- const float32x4_t quantization_max = vdupq_n_f32(255.0f);
- const float32x4_t quantization_mul = vdupq_n_f32(256.0f);
-
- // Uniformly map values to range 8bit integers, i.e. [min, max] -> [0, 255]
- execute_window_loop(window_input_output, [&](const Coordinates & id)
- {
- // Get the input values
- const auto input_ptr = reinterpret_cast<const float *>(input.ptr() + id_batch[1] * _input->info()->strides_in_bytes()[3]);
- float32x4x2_t val = vld2q_f32(input_ptr);
-
- // Map float values to range [0.0, 1.0]
- val.val[0] = vsubq_f32(val.val[0], vmin);
- val.val[1] = vsubq_f32(val.val[1], vmin);
- val.val[0] = vmulq_f32(val.val[0], inv_range);
- val.val[1] = vmulq_f32(val.val[1], inv_range);
-
- // Quantize
- val.val[0] = vmulq_f32(val.val[0], quantization_mul);
- val.val[1] = vmulq_f32(val.val[1], quantization_mul);
- val.val[0] = vminq_f32(val.val[0], quantization_max);
- val.val[1] = vminq_f32(val.val[1], quantization_max);
-
- const uint32x4_t val_u32_low = vcvtq_u32_f32(val.val[0]);
- const uint32x4_t val_u32_high = vcvtq_u32_f32(val.val[1]);
- const uint16x4x2_t val_u16 = vzip_u16(vmovn_u32(val_u32_low), vmovn_u32(val_u32_high));
-
- const uint8x8_t quantized = vmovn_u16(vcombine_u16(val_u16.val[0], val_u16.val[1]));
-
- // Store the quantized values
- auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr() + id_batch[1] * _output->info()->strides_in_bytes()[3]);
- vst1_u8(output_ptr, quantized);
- },
- input, output);
- },
- min_max);
+ case DataType::F32:
+ NEQuantizationLayerKernel::quantize<float>(window, qinfo);
+ break;
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ NEQuantizationLayerKernel::quantize<float16_t>(window, qinfo);
+ break;
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type.");
+ }
}